Overview

Brought to you by YData

Dataset statistics

Number of variables42
Number of observations1000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory535.5 KiB
Average record size in memory548.3 B

Variable types

Numeric16
Categorical26

Alerts

flag has constant value "SF" Constant
land has constant value "0" Constant
wrong_fragment has constant value "0" Constant
urgent has constant value "0" Constant
num_failed_logins has constant value "0" Constant
su_attempted has constant value "0" Constant
num_root has constant value "0" Constant
num_shells has constant value "0" Constant
num_outbound_cmds has constant value "0" Constant
is_host_login has constant value "0" Constant
rerror_rate has constant value "0.0" Constant
srv_rerror_rate has constant value "0.0" Constant
dst_host_rerror_rate has constant value "0.0" Constant
dst_host_srv_rerror_rate has constant value "0.0" Constant
count is highly overall correlated with dst_host_diff_srv_rate and 2 other fieldsHigh correlation
diff_srv_rate is highly overall correlated with same_srv_rateHigh correlation
dst_host_count is highly overall correlated with dst_host_same_src_port_rate and 1 other fieldsHigh correlation
dst_host_diff_srv_rate is highly overall correlated with count and 3 other fieldsHigh correlation
dst_host_same_src_port_rate is highly overall correlated with dst_host_count and 3 other fieldsHigh correlation
dst_host_same_srv_rate is highly overall correlated with count and 3 other fieldsHigh correlation
dst_host_srv_count is highly overall correlated with dst_host_diff_srv_rate and 2 other fieldsHigh correlation
dst_host_srv_diff_host_rate is highly overall correlated with dst_host_count and 5 other fieldsHigh correlation
dst_host_srv_serror_rate is highly overall correlated with duration and 1 other fieldsHigh correlation
duration is highly overall correlated with dst_host_srv_serror_rate and 5 other fieldsHigh correlation
hot is highly overall correlated with duration and 1 other fieldsHigh correlation
is_guest_login is highly overall correlated with hot and 1 other fieldsHigh correlation
logged_in is highly overall correlated with dst_host_same_src_port_rate and 4 other fieldsHigh correlation
num_access_files is highly overall correlated with dst_host_diff_srv_rateHigh correlation
num_compromised is highly overall correlated with dst_host_srv_diff_host_rate and 5 other fieldsHigh correlation
num_file_creations is highly overall correlated with dst_host_srv_diff_host_rate and 6 other fieldsHigh correlation
outcome is highly overall correlated with dst_host_srv_diff_host_rate and 5 other fieldsHigh correlation
protocol_type is highly overall correlated with dst_host_same_src_port_rate and 2 other fieldsHigh correlation
root_shell is highly overall correlated with dst_host_srv_diff_host_rate and 5 other fieldsHigh correlation
same_srv_rate is highly overall correlated with diff_srv_rateHigh correlation
serror_rate is highly overall correlated with srv_serror_rateHigh correlation
service is highly overall correlated with is_guest_login and 6 other fieldsHigh correlation
srv_count is highly overall correlated with countHigh correlation
srv_serror_rate is highly overall correlated with serror_rateHigh correlation
protocol_type is highly imbalanced (79.2%) Imbalance
service is highly imbalanced (68.6%) Imbalance
logged_in is highly imbalanced (67.7%) Imbalance
num_compromised is highly imbalanced (97.9%) Imbalance
root_shell is highly imbalanced (97.1%) Imbalance
num_file_creations is highly imbalanced (98.0%) Imbalance
num_access_files is highly imbalanced (98.9%) Imbalance
is_guest_login is highly imbalanced (90.0%) Imbalance
same_srv_rate is highly imbalanced (98.0%) Imbalance
diff_srv_rate is highly imbalanced (98.0%) Imbalance
dst_host_srv_serror_rate is highly imbalanced (80.7%) Imbalance
outcome is highly imbalanced (97.9%) Imbalance
duration has 960 (96.0%) zeros Zeros
dst_bytes has 37 (3.7%) zeros Zeros
hot has 982 (98.2%) zeros Zeros
serror_rate has 993 (99.3%) zeros Zeros
srv_serror_rate has 992 (99.2%) zeros Zeros
srv_diff_host_rate has 678 (67.8%) zeros Zeros
dst_host_diff_srv_rate has 859 (85.9%) zeros Zeros
dst_host_same_src_port_rate has 303 (30.3%) zeros Zeros
dst_host_srv_diff_host_rate has 380 (38.0%) zeros Zeros
dst_host_serror_rate has 951 (95.1%) zeros Zeros

Reproduction

Analysis started2024-10-21 16:34:47.683948
Analysis finished2024-10-21 16:35:44.993091
Duration57.31 seconds
Software versionydata-profiling vv4.11.0
Download configurationconfig.json

Variables

duration
Real number (ℝ)

High correlation  Zeros 

Distinct18
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.307
Minimum0
Maximum305
Zeros960
Zeros (%)96.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-10-21T22:05:45.121121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum305
Range305
Interquartile range (IQR)0

Descriptive statistics

Standard deviation14.719763
Coefficient of variation (CV)11.262252
Kurtosis298.1204
Mean1.307
Median Absolute Deviation (MAD)0
Skewness16.508156
Sum1307
Variance216.67142
MonotonicityNot monotonic
2024-10-21T22:05:45.298171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 960
96.0%
1 17
 
1.7%
27 4
 
0.4%
20 3
 
0.3%
26 2
 
0.2%
23 2
 
0.2%
79 1
 
0.1%
102 1
 
0.1%
8 1
 
0.1%
7 1
 
0.1%
Other values (8) 8
 
0.8%
ValueCountFrequency (%)
0 960
96.0%
1 17
 
1.7%
3 1
 
0.1%
4 1
 
0.1%
7 1
 
0.1%
8 1
 
0.1%
20 3
 
0.3%
21 1
 
0.1%
23 2
 
0.2%
25 1
 
0.1%
ValueCountFrequency (%)
305 1
 
0.1%
257 1
 
0.1%
184 1
 
0.1%
102 1
 
0.1%
79 1
 
0.1%
29 1
 
0.1%
27 4
0.4%
26 2
0.2%
25 1
 
0.1%
23 2
0.2%

protocol_type
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size58.7 KiB
tcp
950 
udp
 
36
icmp
 
14

Length

Max length4
Median length3
Mean length3.014
Min length3

Characters and Unicode

Total characters3014
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowtcp
2nd rowtcp
3rd rowtcp
4th rowtcp
5th rowtcp

Common Values

ValueCountFrequency (%)
tcp 950
95.0%
udp 36
 
3.6%
icmp 14
 
1.4%

Length

2024-10-21T22:05:45.492808image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:05:45.699633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
tcp 950
95.0%
udp 36
 
3.6%
icmp 14
 
1.4%

Most occurring characters

ValueCountFrequency (%)
p 1000
33.2%
c 964
32.0%
t 950
31.5%
u 36
 
1.2%
d 36
 
1.2%
i 14
 
0.5%
m 14
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3014
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
p 1000
33.2%
c 964
32.0%
t 950
31.5%
u 36
 
1.2%
d 36
 
1.2%
i 14
 
0.5%
m 14
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3014
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
p 1000
33.2%
c 964
32.0%
t 950
31.5%
u 36
 
1.2%
d 36
 
1.2%
i 14
 
0.5%
m 14
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3014
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
p 1000
33.2%
c 964
32.0%
t 950
31.5%
u 36
 
1.2%
d 36
 
1.2%
i 14
 
0.5%
m 14
 
0.5%

service
Categorical

High correlation  Imbalance 

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size59.8 KiB
http
827 
smtp
94 
domain_u
 
29
ftp
 
13
ecr_i
 
10
Other values (5)
 
27

Length

Max length8
Median length4
Mean length4.148
Min length3

Characters and Unicode

Total characters4148
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowhttp
2nd rowhttp
3rd rowhttp
4th rowhttp
5th rowhttp

Common Values

ValueCountFrequency (%)
http 827
82.7%
smtp 94
 
9.4%
domain_u 29
 
2.9%
ftp 13
 
1.3%
ecr_i 10
 
1.0%
finger 8
 
0.8%
ntp_u 7
 
0.7%
auth 4
 
0.4%
telnet 4
 
0.4%
eco_i 4
 
0.4%

Length

2024-10-21T22:05:45.927531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:05:46.101517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
http 827
82.7%
smtp 94
 
9.4%
domain_u 29
 
2.9%
ftp 13
 
1.3%
ecr_i 10
 
1.0%
finger 8
 
0.8%
ntp_u 7
 
0.7%
auth 4
 
0.4%
telnet 4
 
0.4%
eco_i 4
 
0.4%

Most occurring characters

ValueCountFrequency (%)
t 1780
42.9%
p 941
22.7%
h 831
20.0%
m 123
 
3.0%
s 94
 
2.3%
i 51
 
1.2%
_ 50
 
1.2%
n 48
 
1.2%
u 40
 
1.0%
a 33
 
0.8%
Other values (8) 157
 
3.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4148
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t 1780
42.9%
p 941
22.7%
h 831
20.0%
m 123
 
3.0%
s 94
 
2.3%
i 51
 
1.2%
_ 50
 
1.2%
n 48
 
1.2%
u 40
 
1.0%
a 33
 
0.8%
Other values (8) 157
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4148
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t 1780
42.9%
p 941
22.7%
h 831
20.0%
m 123
 
3.0%
s 94
 
2.3%
i 51
 
1.2%
_ 50
 
1.2%
n 48
 
1.2%
u 40
 
1.0%
a 33
 
0.8%
Other values (8) 157
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4148
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t 1780
42.9%
p 941
22.7%
h 831
20.0%
m 123
 
3.0%
s 94
 
2.3%
i 51
 
1.2%
_ 50
 
1.2%
n 48
 
1.2%
u 40
 
1.0%
a 33
 
0.8%
Other values (8) 157
 
3.8%

flag
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.7 KiB
SF
1000 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters2000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSF
2nd rowSF
3rd rowSF
4th rowSF
5th rowSF

Common Values

ValueCountFrequency (%)
SF 1000
100.0%

Length

2024-10-21T22:05:46.398889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:05:46.642069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
sf 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
S 1000
50.0%
F 1000
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1000
50.0%
F 1000
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1000
50.0%
F 1000
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1000
50.0%
F 1000
50.0%

src_bytes
Real number (ℝ)

Distinct310
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean420.11
Minimum0
Maximum19721
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-10-21T22:05:46.792762image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile48
Q1212
median240
Q3309
95-th percentile1411.2
Maximum19721
Range19721
Interquartile range (IQR)97

Descriptive statistics

Standard deviation1067.7281
Coefficient of variation (CV)2.541544
Kurtosis196.49988
Mean420.11
Median Absolute Deviation (MAD)49
Skewness12.849616
Sum420110
Variance1140043.2
MonotonicityNot monotonic
2024-10-21T22:05:47.008341image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
216 19
 
1.9%
30 18
 
1.8%
212 18
 
1.8%
309 15
 
1.5%
317 14
 
1.4%
228 12
 
1.2%
231 12
 
1.2%
219 12
 
1.2%
239 12
 
1.2%
308 11
 
1.1%
Other values (300) 857
85.7%
ValueCountFrequency (%)
0 1
 
0.1%
6 1
 
0.1%
7 1
 
0.1%
8 2
 
0.2%
9 6
 
0.6%
10 2
 
0.2%
29 3
 
0.3%
30 18
1.8%
31 4
 
0.4%
32 4
 
0.4%
ValueCountFrequency (%)
19721 1
0.1%
15744 1
0.1%
15726 1
0.1%
9640 1
0.1%
5519 1
0.1%
4031 1
0.1%
3714 1
0.1%
3591 1
0.1%
3574 1
0.1%
3366 1
0.1%

dst_bytes
Real number (ℝ)

Zeros 

Distinct557
Distinct (%)55.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4283.105
Minimum0
Maximum125015
Zeros37
Zeros (%)3.7%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-10-21T22:05:47.270852image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile48
Q1471.75
median1485.5
Q33698.25
95-th percentile17608.55
Maximum125015
Range125015
Interquartile range (IQR)3226.5

Descriptive statistics

Standard deviation9215.6401
Coefficient of variation (CV)2.151626
Kurtosis52.234493
Mean4283.105
Median Absolute Deviation (MAD)1148.5
Skewness6.0642383
Sum4283105
Variance84928023
MonotonicityNot monotonic
2024-10-21T22:05:47.494053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 37
 
3.7%
316 10
 
1.0%
757 10
 
1.0%
944 9
 
0.9%
891 9
 
0.9%
1680 7
 
0.7%
48 7
 
0.7%
1046 7
 
0.7%
1819 7
 
0.7%
12884 7
 
0.7%
Other values (547) 890
89.0%
ValueCountFrequency (%)
0 37
3.7%
32 2
 
0.2%
35 1
 
0.1%
36 2
 
0.2%
37 1
 
0.1%
39 1
 
0.1%
41 1
 
0.1%
48 7
 
0.7%
75 1
 
0.1%
95 1
 
0.1%
ValueCountFrequency (%)
125015 1
 
0.1%
81172 1
 
0.1%
80476 1
 
0.1%
74810 2
0.2%
74301 1
 
0.1%
61480 2
0.2%
43129 3
0.3%
42747 1
 
0.1%
39873 1
 
0.1%
38125 1
 
0.1%

land
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1000
100.0%

Length

2024-10-21T22:05:47.830722image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:05:48.062354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1000
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

wrong_fragment
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1000
100.0%

Length

2024-10-21T22:05:48.281304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:05:48.407526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1000
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

urgent
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1000
100.0%

Length

2024-10-21T22:05:48.547678image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:05:48.685977image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1000
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

hot
Real number (ℝ)

High correlation  Zeros 

Distinct10
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.188
Minimum0
Maximum30
Zeros982
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-10-21T22:05:48.813085image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum30
Range30
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.8969065
Coefficient of variation (CV)10.089928
Kurtosis168.42578
Mean0.188
Median Absolute Deviation (MAD)0
Skewness12.470957
Sum188
Variance3.5982543
MonotonicityNot monotonic
2024-10-21T22:05:48.983177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 982
98.2%
1 3
 
0.3%
4 3
 
0.3%
6 3
 
0.3%
3 2
 
0.2%
30 2
 
0.2%
14 2
 
0.2%
19 1
 
0.1%
24 1
 
0.1%
18 1
 
0.1%
ValueCountFrequency (%)
0 982
98.2%
1 3
 
0.3%
3 2
 
0.2%
4 3
 
0.3%
6 3
 
0.3%
14 2
 
0.2%
18 1
 
0.1%
19 1
 
0.1%
24 1
 
0.1%
30 2
 
0.2%
ValueCountFrequency (%)
30 2
 
0.2%
24 1
 
0.1%
19 1
 
0.1%
18 1
 
0.1%
14 2
 
0.2%
6 3
 
0.3%
4 3
 
0.3%
3 2
 
0.2%
1 3
 
0.3%
0 982
98.2%

num_failed_logins
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1000
100.0%

Length

2024-10-21T22:05:49.171156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:05:49.312070image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1000
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

logged_in
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
1
941 
0
 
59

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 941
94.1%
0 59
 
5.9%

Length

2024-10-21T22:05:49.456903image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:05:49.602278image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 941
94.1%
0 59
 
5.9%

Most occurring characters

ValueCountFrequency (%)
1 941
94.1%
0 59
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 941
94.1%
0 59
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 941
94.1%
0 59
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 941
94.1%
0 59
 
5.9%

num_compromised
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
998 
2
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 998
99.8%
2 2
 
0.2%

Length

2024-10-21T22:05:49.762744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:05:49.909205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 998
99.8%
2 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
0 998
99.8%
2 2
 
0.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 998
99.8%
2 2
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 998
99.8%
2 2
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 998
99.8%
2 2
 
0.2%

root_shell
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
997 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 997
99.7%
1 3
 
0.3%

Length

2024-10-21T22:05:50.068677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:05:50.213733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 997
99.7%
1 3
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 997
99.7%
1 3
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 997
99.7%
1 3
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 997
99.7%
1 3
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 997
99.7%
1 3
 
0.3%

su_attempted
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1000
100.0%

Length

2024-10-21T22:05:50.372916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:05:50.511883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1000
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

num_root
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1000
100.0%

Length

2024-10-21T22:05:50.662119image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:05:50.804754image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1000
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

num_file_creations
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
997 
1
 
2
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 997
99.7%
1 2
 
0.2%
2 1
 
0.1%

Length

2024-10-21T22:05:50.955124image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:05:51.412954image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 997
99.7%
1 2
 
0.2%
2 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 997
99.7%
1 2
 
0.2%
2 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 997
99.7%
1 2
 
0.2%
2 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 997
99.7%
1 2
 
0.2%
2 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 997
99.7%
1 2
 
0.2%
2 1
 
0.1%

num_shells
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1000
100.0%

Length

2024-10-21T22:05:51.579251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:05:51.803475image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1000
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

num_access_files
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
999 
1
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 999
99.9%
1 1
 
0.1%

Length

2024-10-21T22:05:51.978955image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:05:52.113575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 999
99.9%
1 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 999
99.9%
1 1
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 999
99.9%
1 1
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 999
99.9%
1 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 999
99.9%
1 1
 
0.1%

num_outbound_cmds
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1000
100.0%

Length

2024-10-21T22:05:52.342989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:05:52.483741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1000
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

is_host_login
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
1000 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 1000
100.0%

Length

2024-10-21T22:05:52.615701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:05:52.733515image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 1000
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1000
100.0%

is_guest_login
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
0
987 
1
 
13

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters1000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 987
98.7%
1 13
 
1.3%

Length

2024-10-21T22:05:52.881210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:05:53.086512image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 987
98.7%
1 13
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 987
98.7%
1 13
 
1.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 987
98.7%
1 13
 
1.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 987
98.7%
1 13
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 987
98.7%
1 13
 
1.3%

count
Real number (ℝ)

High correlation 

Distinct40
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.544
Minimum1
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-10-21T22:05:53.242280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median5
Q310.25
95-th percentile26
Maximum41
Range40
Interquartile range (IQR)9.25

Descriptive statistics

Standard deviation8.1659504
Coefficient of variation (CV)1.0824431
Kurtosis3.0445334
Mean7.544
Median Absolute Deviation (MAD)4
Skewness1.7791961
Sum7544
Variance66.682747
MonotonicityNot monotonic
2024-10-21T22:05:53.433187image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
1 265
26.5%
2 93
 
9.3%
3 72
 
7.2%
4 68
 
6.8%
6 51
 
5.1%
5 46
 
4.6%
7 45
 
4.5%
8 45
 
4.5%
11 35
 
3.5%
10 33
 
3.3%
Other values (30) 247
24.7%
ValueCountFrequency (%)
1 265
26.5%
2 93
 
9.3%
3 72
 
7.2%
4 68
 
6.8%
5 46
 
4.6%
6 51
 
5.1%
7 45
 
4.5%
8 45
 
4.5%
9 32
 
3.2%
10 33
 
3.3%
ValueCountFrequency (%)
41 2
 
0.2%
39 3
0.3%
38 1
 
0.1%
37 5
0.5%
36 4
0.4%
35 3
0.3%
34 6
0.6%
33 1
 
0.1%
32 3
0.3%
31 5
0.5%

srv_count
Real number (ℝ)

High correlation 

Distinct46
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.319
Minimum1
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-10-21T22:05:53.675728image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q313
95-th percentile30
Maximum57
Range56
Interquartile range (IQR)11

Descriptive statistics

Standard deviation9.4488369
Coefficient of variation (CV)1.0139325
Kurtosis2.6815563
Mean9.319
Median Absolute Deviation (MAD)5
Skewness1.6302684
Sum9319
Variance89.28052
MonotonicityNot monotonic
2024-10-21T22:05:53.853424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
1 189
18.9%
2 93
 
9.3%
3 66
 
6.6%
4 62
 
6.2%
6 54
 
5.4%
5 50
 
5.0%
7 49
 
4.9%
8 49
 
4.9%
11 38
 
3.8%
9 31
 
3.1%
Other values (36) 319
31.9%
ValueCountFrequency (%)
1 189
18.9%
2 93
9.3%
3 66
 
6.6%
4 62
 
6.2%
5 50
 
5.0%
6 54
 
5.4%
7 49
 
4.9%
8 49
 
4.9%
9 31
 
3.1%
10 30
 
3.0%
ValueCountFrequency (%)
57 1
 
0.1%
55 1
 
0.1%
50 1
 
0.1%
48 1
 
0.1%
42 4
0.4%
41 1
 
0.1%
40 1
 
0.1%
39 3
0.3%
38 2
0.2%
37 4
0.4%

serror_rate
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00069
Minimum0
Maximum0.25
Zeros993
Zeros (%)99.3%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-10-21T22:05:54.004888image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.25
Range0.25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.010494476
Coefficient of variation (CV)15.209386
Kurtosis399.15936
Mean0.00069
Median Absolute Deviation (MAD)0
Skewness19.069925
Sum0.69
Variance0.00011013403
MonotonicityNot monotonic
2024-10-21T22:05:54.206735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 993
99.3%
0.03 2
 
0.2%
0.25 1
 
0.1%
0.11 1
 
0.1%
0.17 1
 
0.1%
0.06 1
 
0.1%
0.04 1
 
0.1%
ValueCountFrequency (%)
0 993
99.3%
0.03 2
 
0.2%
0.04 1
 
0.1%
0.06 1
 
0.1%
0.11 1
 
0.1%
0.17 1
 
0.1%
0.25 1
 
0.1%
ValueCountFrequency (%)
0.25 1
 
0.1%
0.17 1
 
0.1%
0.11 1
 
0.1%
0.06 1
 
0.1%
0.04 1
 
0.1%
0.03 2
 
0.2%
0 993
99.3%

srv_serror_rate
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00072
Minimum0
Maximum0.25
Zeros992
Zeros (%)99.2%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-10-21T22:05:54.341893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.25
Range0.25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.010535302
Coefficient of variation (CV)14.632364
Kurtosis392.80623
Mean0.00072
Median Absolute Deviation (MAD)0
Skewness18.862125
Sum0.72
Variance0.00011099259
MonotonicityNot monotonic
2024-10-21T22:05:54.571345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 992
99.2%
0.03 3
 
0.3%
0.25 1
 
0.1%
0.11 1
 
0.1%
0.17 1
 
0.1%
0.06 1
 
0.1%
0.04 1
 
0.1%
ValueCountFrequency (%)
0 992
99.2%
0.03 3
 
0.3%
0.04 1
 
0.1%
0.06 1
 
0.1%
0.11 1
 
0.1%
0.17 1
 
0.1%
0.25 1
 
0.1%
ValueCountFrequency (%)
0.25 1
 
0.1%
0.17 1
 
0.1%
0.11 1
 
0.1%
0.06 1
 
0.1%
0.04 1
 
0.1%
0.03 3
 
0.3%
0 992
99.2%

rerror_rate
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size58.7 KiB
0.0
1000 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1000
100.0%

Length

2024-10-21T22:05:54.865671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:05:55.083287image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2000
66.7%
. 1000
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2000
66.7%
. 1000
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2000
66.7%
. 1000
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2000
66.7%
. 1000
33.3%

srv_rerror_rate
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size58.7 KiB
0.0
1000 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1000
100.0%

Length

2024-10-21T22:05:55.333960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:05:55.498938image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2000
66.7%
. 1000
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2000
66.7%
. 1000
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2000
66.7%
. 1000
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2000
66.7%
. 1000
33.3%

same_srv_rate
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size58.7 KiB
1.0
997 
0.5
 
2
0.33
 
1

Length

Max length4
Median length3
Mean length3.001
Min length3

Characters and Unicode

Total characters3001
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 997
99.7%
0.5 2
 
0.2%
0.33 1
 
0.1%

Length

2024-10-21T22:05:55.625505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:05:55.756700image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 997
99.7%
0.5 2
 
0.2%
0.33 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
. 1000
33.3%
0 1000
33.3%
1 997
33.2%
5 2
 
0.1%
3 2
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3001
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
. 1000
33.3%
0 1000
33.3%
1 997
33.2%
5 2
 
0.1%
3 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3001
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
. 1000
33.3%
0 1000
33.3%
1 997
33.2%
5 2
 
0.1%
3 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3001
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
. 1000
33.3%
0 1000
33.3%
1 997
33.2%
5 2
 
0.1%
3 2
 
0.1%

diff_srv_rate
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size58.7 KiB
0.0
997 
1.0
 
2
0.67
 
1

Length

Max length4
Median length3
Mean length3.001
Min length3

Characters and Unicode

Total characters3001
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 997
99.7%
1.0 2
 
0.2%
0.67 1
 
0.1%

Length

2024-10-21T22:05:55.897658image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:05:56.030229image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 997
99.7%
1.0 2
 
0.2%
0.67 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1997
66.5%
. 1000
33.3%
1 2
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3001
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1997
66.5%
. 1000
33.3%
1 2
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3001
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1997
66.5%
. 1000
33.3%
1 2
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3001
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1997
66.5%
. 1000
33.3%
1 2
 
0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%

srv_diff_host_rate
Real number (ℝ)

Zeros 

Distinct36
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.12016
Minimum0
Maximum1
Zeros678
Zeros (%)67.8%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-10-21T22:05:56.176969image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.12
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.12

Descriptive statistics

Standard deviation0.25590401
Coefficient of variation (CV)2.1296938
Kurtosis5.6436977
Mean0.12016
Median Absolute Deviation (MAD)0
Skewness2.5560753
Sum120.16
Variance0.065486861
MonotonicityNot monotonic
2024-10-21T22:05:56.408281image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 678
67.8%
1 57
 
5.7%
0.2 15
 
1.5%
0.12 15
 
1.5%
0.18 15
 
1.5%
0.4 15
 
1.5%
0.25 14
 
1.4%
0.14 14
 
1.4%
0.22 14
 
1.4%
0.33 13
 
1.3%
Other values (26) 150
 
15.0%
ValueCountFrequency (%)
0 678
67.8%
0.05 4
 
0.4%
0.06 8
 
0.8%
0.07 13
 
1.3%
0.08 9
 
0.9%
0.09 7
 
0.7%
0.1 12
 
1.2%
0.11 12
 
1.2%
0.12 15
 
1.5%
0.13 8
 
0.8%
ValueCountFrequency (%)
1 57
5.7%
0.75 6
 
0.6%
0.67 10
 
1.0%
0.62 1
 
0.1%
0.6 3
 
0.3%
0.57 1
 
0.1%
0.55 1
 
0.1%
0.5 11
 
1.1%
0.43 3
 
0.3%
0.4 15
 
1.5%

dst_host_count
Real number (ℝ)

High correlation 

Distinct180
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean112.16
Minimum1
Maximum255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-10-21T22:05:56.639992image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q121
median66
Q3255
95-th percentile255
Maximum255
Range254
Interquartile range (IQR)234

Descriptive statistics

Standard deviation101.69263
Coefficient of variation (CV)0.9066747
Kurtosis-1.5152501
Mean112.16
Median Absolute Deviation (MAD)57
Skewness0.46806664
Sum112160
Variance10341.392
MonotonicityNot monotonic
2024-10-21T22:05:56.925006image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 276
27.6%
1 22
 
2.2%
2 18
 
1.8%
4 15
 
1.5%
8 14
 
1.4%
11 14
 
1.4%
6 14
 
1.4%
10 13
 
1.3%
12 13
 
1.3%
22 13
 
1.3%
Other values (170) 588
58.8%
ValueCountFrequency (%)
1 22
2.2%
2 18
1.8%
3 12
1.2%
4 15
1.5%
5 11
1.1%
6 14
1.4%
7 12
1.2%
8 14
1.4%
9 10
1.0%
10 13
1.3%
ValueCountFrequency (%)
255 276
27.6%
254 1
 
0.1%
252 2
 
0.2%
251 1
 
0.1%
246 2
 
0.2%
244 1
 
0.1%
242 2
 
0.2%
241 1
 
0.1%
236 2
 
0.2%
234 1
 
0.1%

dst_host_srv_count
Real number (ℝ)

High correlation 

Distinct151
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean208.097
Minimum1
Maximum255
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-10-21T22:05:57.145929image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile23.85
Q1182
median255
Q3255
95-th percentile255
Maximum255
Range254
Interquartile range (IQR)73

Descriptive statistics

Standard deviation81.204789
Coefficient of variation (CV)0.39022566
Kurtosis0.45726379
Mean208.097
Median Absolute Deviation (MAD)0
Skewness-1.4354651
Sum208097
Variance6594.2178
MonotonicityNot monotonic
2024-10-21T22:05:57.383307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 699
69.9%
15 7
 
0.7%
156 7
 
0.7%
187 6
 
0.6%
17 5
 
0.5%
157 5
 
0.5%
177 5
 
0.5%
47 5
 
0.5%
182 5
 
0.5%
36 4
 
0.4%
Other values (141) 252
 
25.2%
ValueCountFrequency (%)
1 1
 
0.1%
2 3
0.3%
3 3
0.3%
4 2
0.2%
5 3
0.3%
6 1
 
0.1%
7 3
0.3%
8 1
 
0.1%
9 2
0.2%
10 1
 
0.1%
ValueCountFrequency (%)
255 699
69.9%
249 1
 
0.1%
247 2
 
0.2%
246 1
 
0.1%
239 1
 
0.1%
237 2
 
0.2%
236 1
 
0.1%
229 1
 
0.1%
227 3
 
0.3%
226 1
 
0.1%

dst_host_same_srv_rate
Real number (ℝ)

High correlation 

Distinct67
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.94426
Minimum0.06
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-10-21T22:05:57.901167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.06
5-th percentile0.5795
Q11
median1
Q31
95-th percentile1
Maximum1
Range0.94
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.16903024
Coefficient of variation (CV)0.17900816
Kurtosis11.364583
Mean0.94426
Median Absolute Deviation (MAD)0
Skewness-3.4167015
Sum944.26
Variance0.028571224
MonotonicityNot monotonic
2024-10-21T22:05:58.132907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 859
85.9%
0.8 6
 
0.6%
0.67 5
 
0.5%
0.5 5
 
0.5%
0.68 5
 
0.5%
0.22 5
 
0.5%
0.64 5
 
0.5%
0.86 4
 
0.4%
0.69 4
 
0.4%
0.93 4
 
0.4%
Other values (57) 98
 
9.8%
ValueCountFrequency (%)
0.06 1
 
0.1%
0.09 1
 
0.1%
0.1 1
 
0.1%
0.11 1
 
0.1%
0.13 1
 
0.1%
0.14 3
0.3%
0.15 3
0.3%
0.17 2
0.2%
0.18 2
0.2%
0.2 1
 
0.1%
ValueCountFrequency (%)
1 859
85.9%
0.99 2
 
0.2%
0.97 1
 
0.1%
0.96 1
 
0.1%
0.95 3
 
0.3%
0.94 4
 
0.4%
0.93 4
 
0.4%
0.92 2
 
0.2%
0.91 1
 
0.1%
0.9 2
 
0.2%

dst_host_diff_srv_rate
Real number (ℝ)

High correlation  Zeros 

Distinct31
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.01863
Minimum0
Maximum1
Zeros859
Zeros (%)85.9%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-10-21T22:05:58.322858image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.11
Maximum1
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.071235508
Coefficient of variation (CV)3.8236988
Kurtosis71.304693
Mean0.01863
Median Absolute Deviation (MAD)0
Skewness7.307053
Sum18.63
Variance0.0050744976
MonotonicityNot monotonic
2024-10-21T22:05:58.513594image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 859
85.9%
0.04 15
 
1.5%
0.05 13
 
1.3%
0.06 12
 
1.2%
0.07 11
 
1.1%
0.09 11
 
1.1%
0.11 11
 
1.1%
0.08 9
 
0.9%
0.15 7
 
0.7%
0.1 6
 
0.6%
Other values (21) 46
 
4.6%
ValueCountFrequency (%)
0 859
85.9%
0.02 3
 
0.3%
0.03 4
 
0.4%
0.04 15
 
1.5%
0.05 13
 
1.3%
0.06 12
 
1.2%
0.07 11
 
1.1%
0.08 9
 
0.9%
0.09 11
 
1.1%
0.1 6
 
0.6%
ValueCountFrequency (%)
1 1
 
0.1%
0.75 2
0.2%
0.67 1
 
0.1%
0.5 2
0.2%
0.4 2
0.2%
0.36 1
 
0.1%
0.33 2
0.2%
0.3 1
 
0.1%
0.29 1
 
0.1%
0.25 3
0.3%

dst_host_same_src_port_rate
Real number (ℝ)

High correlation  Zeros 

Distinct34
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09589
Minimum0
Maximum1
Zeros303
Zeros (%)30.3%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-10-21T22:05:58.690368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.02
Q30.06
95-th percentile0.701
Maximum1
Range1
Interquartile range (IQR)0.06

Descriptive statistics

Standard deviation0.22323943
Coefficient of variation (CV)2.3280783
Kurtosis10.067192
Mean0.09589
Median Absolute Deviation (MAD)0.02
Skewness3.2937819
Sum95.89
Variance0.049835844
MonotonicityNot monotonic
2024-10-21T22:05:58.904958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 303
30.3%
0.01 177
17.7%
0.02 104
 
10.4%
0.03 71
 
7.1%
1 44
 
4.4%
0.04 42
 
4.2%
0.05 38
 
3.8%
0.06 29
 
2.9%
0.08 25
 
2.5%
0.5 18
 
1.8%
Other values (24) 149
14.9%
ValueCountFrequency (%)
0 303
30.3%
0.01 177
17.7%
0.02 104
 
10.4%
0.03 71
 
7.1%
0.04 42
 
4.2%
0.05 38
 
3.8%
0.06 29
 
2.9%
0.07 15
 
1.5%
0.08 25
 
2.5%
0.09 13
 
1.3%
ValueCountFrequency (%)
1 44
4.4%
0.99 2
 
0.2%
0.79 1
 
0.1%
0.75 1
 
0.1%
0.73 1
 
0.1%
0.72 1
 
0.1%
0.7 1
 
0.1%
0.64 1
 
0.1%
0.57 1
 
0.1%
0.5 18
1.8%

dst_host_srv_diff_host_rate
Real number (ℝ)

High correlation  Zeros 

Distinct23
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0259
Minimum0
Maximum1
Zeros380
Zeros (%)38.0%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-10-21T22:05:59.096186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.02
Q30.04
95-th percentile0.07
Maximum1
Range1
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.052367283
Coefficient of variation (CV)2.0219028
Kurtosis155.70878
Mean0.0259
Median Absolute Deviation (MAD)0.02
Skewness10.430138
Sum25.9
Variance0.0027423323
MonotonicityNot monotonic
2024-10-21T22:05:59.415476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 380
38.0%
0.02 171
17.1%
0.04 138
 
13.8%
0.03 114
 
11.4%
0.05 79
 
7.9%
0.01 51
 
5.1%
0.06 16
 
1.6%
0.09 12
 
1.2%
0.08 10
 
1.0%
0.07 6
 
0.6%
Other values (13) 23
 
2.3%
ValueCountFrequency (%)
0 380
38.0%
0.01 51
 
5.1%
0.02 171
17.1%
0.03 114
 
11.4%
0.04 138
 
13.8%
0.05 79
 
7.9%
0.06 16
 
1.6%
0.07 6
 
0.6%
0.08 10
 
1.0%
0.09 12
 
1.2%
ValueCountFrequency (%)
1 1
 
0.1%
0.67 1
 
0.1%
0.5 1
 
0.1%
0.4 2
0.2%
0.33 1
 
0.1%
0.31 1
 
0.1%
0.29 1
 
0.1%
0.18 2
0.2%
0.17 2
0.2%
0.13 3
0.3%

dst_host_serror_rate
Real number (ℝ)

Zeros 

Distinct8
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00101
Minimum0
Maximum0.1
Zeros951
Zeros (%)95.1%
Negative0
Negative (%)0.0%
Memory size7.9 KiB
2024-10-21T22:05:59.607917image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum0.1
Range0.1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0060594238
Coefficient of variation (CV)5.9994295
Kurtosis106.7839
Mean0.00101
Median Absolute Deviation (MAD)0
Skewness9.2006124
Sum1.01
Variance3.6716617 × 10-5
MonotonicityNot monotonic
2024-10-21T22:05:59.781398image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 951
95.1%
0.01 32
 
3.2%
0.03 6
 
0.6%
0.02 3
 
0.3%
0.05 3
 
0.3%
0.06 2
 
0.2%
0.04 2
 
0.2%
0.1 1
 
0.1%
ValueCountFrequency (%)
0 951
95.1%
0.01 32
 
3.2%
0.02 3
 
0.3%
0.03 6
 
0.6%
0.04 2
 
0.2%
0.05 3
 
0.3%
0.06 2
 
0.2%
0.1 1
 
0.1%
ValueCountFrequency (%)
0.1 1
 
0.1%
0.06 2
 
0.2%
0.05 3
 
0.3%
0.04 2
 
0.2%
0.03 6
 
0.6%
0.02 3
 
0.3%
0.01 32
 
3.2%
0 951
95.1%

dst_host_srv_serror_rate
Categorical

High correlation  Imbalance 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size58.8 KiB
0.0
947 
0.01
 
52
0.13
 
1

Length

Max length4
Median length3
Mean length3.053
Min length3

Characters and Unicode

Total characters3053
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 947
94.7%
0.01 52
 
5.2%
0.13 1
 
0.1%

Length

2024-10-21T22:06:00.122289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:06:00.331504image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 947
94.7%
0.01 52
 
5.2%
0.13 1
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 1999
65.5%
. 1000
32.8%
1 53
 
1.7%
3 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3053
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 1999
65.5%
. 1000
32.8%
1 53
 
1.7%
3 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3053
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 1999
65.5%
. 1000
32.8%
1 53
 
1.7%
3 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3053
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 1999
65.5%
. 1000
32.8%
1 53
 
1.7%
3 1
 
< 0.1%

dst_host_rerror_rate
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size58.7 KiB
0.0
1000 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1000
100.0%

Length

2024-10-21T22:06:00.566874image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:06:00.824347image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2000
66.7%
. 1000
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2000
66.7%
. 1000
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2000
66.7%
. 1000
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2000
66.7%
. 1000
33.3%

dst_host_srv_rerror_rate
Categorical

Constant 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size58.7 KiB
0.0
1000 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters3000
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 1000
100.0%

Length

2024-10-21T22:06:01.064112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:06:01.273201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 1000
100.0%

Most occurring characters

ValueCountFrequency (%)
0 2000
66.7%
. 1000
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2000
66.7%
. 1000
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2000
66.7%
. 1000
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2000
66.7%
. 1000
33.3%

outcome
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size62.6 KiB
normal.
998 
buffer_overflow.
 
2

Length

Max length16
Median length7
Mean length7.018
Min length7

Characters and Unicode

Total characters7018
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rownormal.
2nd rownormal.
3rd rownormal.
4th rownormal.
5th rownormal.

Common Values

ValueCountFrequency (%)
normal. 998
99.8%
buffer_overflow. 2
 
0.2%

Length

2024-10-21T22:06:01.454531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-21T22:06:01.577979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
normal 998
99.8%
buffer_overflow 2
 
0.2%

Most occurring characters

ValueCountFrequency (%)
o 1002
14.3%
r 1002
14.3%
l 1000
14.2%
. 1000
14.2%
m 998
14.2%
n 998
14.2%
a 998
14.2%
f 6
 
0.1%
e 4
 
0.1%
b 2
 
< 0.1%
Other values (4) 8
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7018
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1002
14.3%
r 1002
14.3%
l 1000
14.2%
. 1000
14.2%
m 998
14.2%
n 998
14.2%
a 998
14.2%
f 6
 
0.1%
e 4
 
0.1%
b 2
 
< 0.1%
Other values (4) 8
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7018
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1002
14.3%
r 1002
14.3%
l 1000
14.2%
. 1000
14.2%
m 998
14.2%
n 998
14.2%
a 998
14.2%
f 6
 
0.1%
e 4
 
0.1%
b 2
 
< 0.1%
Other values (4) 8
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7018
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1002
14.3%
r 1002
14.3%
l 1000
14.2%
. 1000
14.2%
m 998
14.2%
n 998
14.2%
a 998
14.2%
f 6
 
0.1%
e 4
 
0.1%
b 2
 
< 0.1%
Other values (4) 8
 
0.1%

Interactions

2024-10-21T22:05:40.088176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:51.481755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:54.971525image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:58.654440image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:02.118682image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:05.130353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:08.694589image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:11.932735image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:14.779632image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:18.608424image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:22.068841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:25.191559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:28.134131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:31.131016image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:34.055960image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:36.934265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:40.242935image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:51.908931image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:55.280683image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:58.869814image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:02.276083image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:05.289274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:08.959032image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:12.107607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:14.941854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:18.754966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:22.725407image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:25.337277image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:28.303937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:31.302408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:34.236495image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:37.092078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:40.442709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:52.276809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:55.461636image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:59.279583image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:02.444489image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:05.474106image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:09.144527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:12.290845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:15.110232image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:19.079383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:22.943701image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:25.505670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:28.489534image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:31.486491image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:34.400661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:37.280168image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:40.604518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:52.447108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:55.636561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:59.415631image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:02.720742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:05.781839image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:09.385183image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:12.459567image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:15.270380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:19.238447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:23.105864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:25.668659image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:28.660364image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:31.657897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:34.550059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:37.669094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:40.759509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:52.663274image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:55.804437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:59.732559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:03.026361image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:06.490648image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:09.654383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:12.622205image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:15.439334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:19.481712image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:23.264627image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:25.843794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:28.818716image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:31.845537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:34.699681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:37.824970image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:41.005529image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:52.829826image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:55.986421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:00.041642image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:03.343499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:06.774290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:09.982029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:12.797084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:15.620416image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:19.765034image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:23.437160image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:26.015421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:28.975323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:32.020094image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:34.867926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:37.983434image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:41.297756image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:52.980392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:56.255941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:00.357850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:03.495664image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:06.933265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:10.125704image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:12.955403image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:15.799638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:19.900719image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:23.708465image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:26.166291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:29.113844image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:32.201908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:35.014703image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:38.128488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:41.640582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:53.114397image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:56.592046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:00.533669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:03.637542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:07.096893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:10.295208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:13.113776image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:16.086563image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:20.045186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:23.853661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:26.324743image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:29.258637image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:32.450260image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:35.162279image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:38.278259image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:41.846437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:53.252699image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:56.914982image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:00.700421image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:03.811176image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:07.331714image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:10.572263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:13.327633image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:16.395096image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:20.188928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:23.992748image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:26.481588image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:29.735338image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:32.737774image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:35.416120image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:38.420964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:42.087961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:53.390258image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:57.233881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:01.008184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:04.037447image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:07.527816image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:10.824562image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:13.697422image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:16.682813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:20.453351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:24.137046image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:26.641528image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:29.916603image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:32.919117image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:35.736410image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:38.557498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:42.359738image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:53.526921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:57.576476image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:01.161358image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:04.195352image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:07.706331image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:10.983830image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:13.855885image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:16.990836image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:20.754841image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:24.281705image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:26.795078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:30.071075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:33.079507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:35.933134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:38.708780image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:42.516626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:53.671356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:57.779597image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:01.316561image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:04.353582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:07.873503image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:11.141390image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:14.016112image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:17.286235image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:21.044568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:24.436188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:26.952178image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:30.221685image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:33.225305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:36.112468image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:38.935625image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:42.666958image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:53.842041image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:57.928742image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:01.466937image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:04.505256image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:08.035020image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:11.292864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:14.171502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:17.554056image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:21.300451image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:24.583141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:27.270427image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:30.359339image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:33.402765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:36.274029image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:39.251961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:42.821399image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:54.180082image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:58.161921image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:01.632517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:04.669707image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:08.205617image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:11.454909image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:14.331484image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:17.844091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:21.574669image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:24.736091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:27.568093image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:30.506607image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:33.565805image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:36.440290image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:39.520239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:42.975565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:54.514614image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:58.348492image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:01.794437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:04.825640image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:08.367194image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:11.617066image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:14.475420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:18.218644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:21.774746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:24.898941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:27.806979image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:30.674598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:33.730825image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:36.628507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:39.718584image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:43.126621image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:54.715677image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:04:58.491496image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:01.961396image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:04.978896image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:08.533843image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:11.776045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:14.625114image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:18.463615image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:21.906004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:25.051191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:27.968335image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:30.970467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:33.891838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:36.785928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-21T22:05:39.944253image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-21T22:06:01.713019image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
countdiff_srv_ratedst_bytesdst_host_countdst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_same_srv_ratedst_host_serror_ratedst_host_srv_countdst_host_srv_diff_host_ratedst_host_srv_serror_ratedurationhotis_guest_loginlogged_innum_access_filesnum_compromisednum_file_creationsoutcomeprotocol_typeroot_shellsame_srv_rateserror_rateservicesrc_bytessrv_countsrv_diff_host_ratesrv_serror_rate
count1.0000.0000.2490.383-0.511-0.4290.5100.0840.487-0.2270.042-0.248-0.1300.0450.2090.0000.0000.0000.0000.1140.0000.0000.0930.103-0.0640.890-0.0910.080
diff_srv_rate0.0001.0000.0000.0000.0780.0000.2780.0000.0860.2770.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.4250.0000.0000.0000.000
dst_bytes0.2490.0001.0000.086-0.450-0.1760.4510.1170.339-0.0350.157-0.1090.0240.0000.0000.0000.0000.0000.0000.0000.0000.0000.0380.000-0.0360.191-0.1370.052
dst_host_count0.3830.0000.0861.000-0.212-0.9460.1930.1140.378-0.6790.057-0.106-0.0540.0870.1990.0000.0000.0000.0000.1360.0000.0000.0690.111-0.0900.3250.0110.047
dst_host_diff_srv_rate-0.5110.078-0.450-0.2121.0000.254-0.993-0.012-0.6310.0780.0000.3380.1880.0200.2540.7020.0000.0000.0000.1250.0000.078-0.0340.3240.235-0.4750.071-0.036
dst_host_same_src_port_rate-0.4290.000-0.176-0.9460.2541.000-0.245-0.120-0.4520.6240.0000.1060.0420.0000.6120.1970.1700.1020.1700.5620.1230.000-0.0680.434-0.022-0.3650.020-0.046
dst_host_same_srv_rate0.5100.2780.4510.193-0.993-0.2451.0000.0100.636-0.0660.173-0.355-0.2140.4880.5760.3180.0000.1630.0000.3980.0000.2780.0340.447-0.2090.475-0.0690.036
dst_host_serror_rate0.0840.0000.1170.114-0.012-0.1200.0101.0000.039-0.0020.335-0.021-0.0310.0000.0000.0000.0000.2780.0000.0000.0000.0000.0940.0350.0830.066-0.0670.086
dst_host_srv_count0.4870.0860.3390.378-0.631-0.4520.6360.0391.000-0.1080.107-0.310-0.2230.2920.5810.1490.1540.1240.1540.3750.1070.0860.0540.338-0.0520.449-0.0580.058
dst_host_srv_diff_host_rate-0.2270.277-0.035-0.6790.0780.624-0.066-0.002-0.1081.0000.223-0.033-0.0500.0000.3150.0000.9970.7420.9970.1010.8130.277-0.0420.4910.045-0.1690.039-0.028
dst_host_srv_serror_rate0.0420.0000.1570.0570.0000.0000.1730.3350.1070.2231.0000.7040.0000.0000.0390.0000.0000.7060.0000.0000.0000.0000.0660.3490.0000.1180.0300.076
duration-0.2480.000-0.109-0.1060.3380.106-0.355-0.021-0.310-0.0330.7041.0000.5610.0000.0000.0000.9980.9980.9980.0000.8130.000-0.0170.3760.180-0.2160.059-0.018
hot-0.1300.0000.024-0.0540.1880.042-0.214-0.031-0.223-0.0500.0000.5611.0000.9970.0000.0000.0000.0000.0000.0000.0000.000-0.0110.3980.119-0.138-0.063-0.012
is_guest_login0.0450.0000.0000.0870.0200.0000.4880.0000.2920.0000.0000.0000.9971.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.9960.0000.0590.0000.000
logged_in0.2090.0000.0000.1990.2540.6120.5760.0000.5810.3150.0390.0000.0000.0001.0000.0000.0000.0000.0000.9160.0000.0000.0000.9870.0000.2150.2330.000
num_access_files0.0000.0000.0000.0000.7020.1970.3180.0000.1490.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.0250.0000.0000.0930.000
num_compromised0.0000.0000.0000.0000.0000.1700.0000.0000.1540.9970.0000.9980.0000.0000.0000.0001.0000.9990.7490.0000.6110.0000.0000.7000.0000.0000.0000.000
num_file_creations0.0000.0000.0000.0000.0000.1020.1630.2780.1240.7420.7060.9980.0000.0000.0000.0000.9991.0000.9990.0000.8150.0000.0000.6050.0000.0000.0000.000
outcome0.0000.0000.0000.0000.0000.1700.0000.0000.1540.9970.0000.9980.0000.0000.0000.0000.7490.9991.0000.0000.6110.0000.0000.7000.0000.0000.0000.000
protocol_type0.1140.0000.0000.1360.1250.5620.3980.0000.3750.1010.0000.0000.0000.0000.9160.0000.0000.0000.0001.0000.0000.0000.0000.9960.0000.1180.2300.000
root_shell0.0000.0000.0000.0000.0000.1230.0000.0000.1070.8130.0000.8130.0000.0000.0000.0000.6110.8150.6110.0001.0000.0000.0000.5680.0000.0000.0000.000
same_srv_rate0.0001.0000.0000.0000.0780.0000.2780.0000.0860.2770.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.0000.4250.0000.0000.0000.000
serror_rate0.0930.0000.0380.069-0.034-0.0680.0340.0940.054-0.0420.066-0.017-0.0110.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.000-0.0460.079-0.0560.936
service0.1030.4250.0000.1110.3240.4340.4470.0350.3380.4910.3490.3760.3980.9960.9870.0250.7000.6050.7000.9960.5680.4250.0001.0000.1860.1120.1930.000
src_bytes-0.0640.000-0.036-0.0900.235-0.022-0.2090.083-0.0520.0450.0000.1800.1190.0000.0000.0000.0000.0000.0000.0000.0000.000-0.0460.1861.000-0.0830.010-0.033
srv_count0.8900.0000.1910.325-0.475-0.3650.4750.0660.449-0.1690.118-0.216-0.1380.0590.2150.0000.0000.0000.0000.1180.0000.0000.0790.112-0.0831.0000.2090.092
srv_diff_host_rate-0.0910.000-0.1370.0110.0710.020-0.069-0.067-0.0580.0390.0300.059-0.0630.0000.2330.0930.0000.0000.0000.2300.0000.000-0.0560.1930.0100.2091.000-0.044
srv_serror_rate0.0800.0000.0520.047-0.036-0.0460.0360.0860.058-0.0280.076-0.018-0.0120.0000.0000.0000.0000.0000.0000.0000.0000.0000.9360.000-0.0330.092-0.0441.000

Missing values

2024-10-21T22:05:43.620897image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-21T22:05:44.660206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateoutcome
00tcphttpSF18154500000010000000000880.00.00.00.01.00.00.0991.00.00.110.000.00.00.00.0normal.
10tcphttpSF2394860000010000000000880.00.00.00.01.00.00.019191.00.00.050.000.00.00.00.0normal.
20tcphttpSF23513370000010000000000880.00.00.00.01.00.00.029291.00.00.030.000.00.00.00.0normal.
30tcphttpSF21913370000010000000000660.00.00.00.01.00.00.039391.00.00.030.000.00.00.00.0normal.
40tcphttpSF21720320000010000000000660.00.00.00.01.00.00.049491.00.00.020.000.00.00.00.0normal.
50tcphttpSF21720320000010000000000660.00.00.00.01.00.00.059591.00.00.020.000.00.00.00.0normal.
60tcphttpSF21219400000010000000000120.00.00.00.01.00.01.01691.00.01.000.040.00.00.00.0normal.
70tcphttpSF15940870000010000000000550.00.00.00.01.00.00.011791.00.00.090.040.00.00.00.0normal.
80tcphttpSF2101510000010000000000880.00.00.00.01.00.00.08891.00.00.120.040.00.00.00.0normal.
90tcphttpSF2127860001010000000000880.00.00.00.01.00.00.08991.00.00.120.050.00.00.00.0normal.
durationprotocol_typeserviceflagsrc_bytesdst_byteslandwrong_fragmenturgenthotnum_failed_loginslogged_innum_compromisedroot_shellsu_attemptednum_rootnum_file_creationsnum_shellsnum_access_filesnum_outbound_cmdsis_host_loginis_guest_logincountsrv_countserror_ratesrv_serror_ratererror_ratesrv_rerror_ratesame_srv_ratediff_srv_ratesrv_diff_host_ratedst_host_countdst_host_srv_countdst_host_same_srv_ratedst_host_diff_srv_ratedst_host_same_src_port_ratedst_host_srv_diff_host_ratedst_host_serror_ratedst_host_srv_serror_ratedst_host_rerror_ratedst_host_srv_rerror_rateoutcome
9900tcphttpSF29156570000010000000000550.00.00.00.01.00.00.0071671.00.00.140.100.00.00.00.0normal.
9910tcphttpSF32814330000010000000000660.00.00.00.01.00.00.0061771.00.00.170.100.00.00.00.0normal.
9920tcphttpSF21121010000010000000000440.00.00.00.01.00.00.00161871.00.00.060.090.00.00.00.0normal.
9930tcphttpSF32524520000010000000000230.00.00.00.01.00.00.67261971.00.00.040.090.00.00.00.0normal.
9940tcphttpSF340229650000010000000000110.00.00.00.01.00.00.00362071.00.00.030.080.00.00.00.0normal.
9950tcphttpSF2351028000001000000000010100.00.00.00.01.00.00.00102171.00.00.100.080.00.00.00.0normal.
9960tcphttpSF2231275000001000000000020200.00.00.00.01.00.00.00202271.00.00.050.080.00.00.00.0normal.
9970tcphttpSF22640060000010000000000240.00.00.00.01.00.00.75302371.00.00.030.080.00.00.00.0normal.
9980tcphttpSF2315283000101010000000012140.00.00.00.01.00.00.21402471.00.00.030.070.00.00.00.0normal.
9990tcphttpSF2305558000001000000000022240.00.00.00.01.00.00.12502551.00.00.020.070.00.00.00.0normal.